@article {2022,
	title = {MERP: A Music Dataset with Emotion Ratings and Raters{\textquoteright} Profile Information},
	journal = {Sensors - Intelligent Sensors},
	volume = {23},
	year = {2023},
	month = {01/2023},
	chapter = {382},
	abstract = {Music is capable of conveying many emotions. The level and type of emotion of the music perceived by a listener, however, is highly subjective. In this study, we present the Music Emotion Recognition with Profile information dataset (MERP). This database was collected through Amazon Mechanical Turk (MTurk) and features dynamical valence and arousal ratings of 54 selected full-length songs. The dataset contains music features, as well as user profile information of the annotators. The songs were selected from the Free Music Archive using an innovative method (a Triple Neural Network with the OpenSmile toolkit) to identify 50 songs with the most distinctive emotions. Specifically, the songs were chosen to fully cover the four quadrants of the valence arousal space. Four additional songs were selected from DEAM to act as a benchmark in this study and filter out low quality ratings. A total of 277 participants participated in annotating the dataset, and their demographic information, listening preferences, and musical background were recorded. We offer an extensive analysis of the resulting dataset, together with a baseline emotion prediction model based on a fully connected model and an LSTM model, for our newly proposed MERP dataset.},
	doi = {10.3390/s23010382},
	url = {https://www.mdpi.com/1424-8220/23/1/382},
	author = {E. Koh and K.W. Cheuk and K.Y. Heung and K. Agres and D. Herremans}
}
@article {2022,
	title = {Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses},
	journal = {Arxiv preprint},
	year = {2022},
	abstract = {Although media content is increasingly produced, distributed, and consumed in multiple combinations of modalities, how individual modalities contribute to the perceived emotion of a media item remains poorly understood. In this paper we present MusicVideos (MuVi), a novel dataset for affective multimedia content analysis to study how the auditory and visual modalities contribute to the perceived emotion of media. The data were collected by presenting music videos to participants in three conditions: music, visual, and audiovisual. Participants annotated the music videos for valence and arousal over time, as well as the overall emotion conveyed. We present detailed descriptive statistics for key measures in the dataset and the results of feature importance analyses for each condition. Finally, we propose a novel transfer learning architecture to train Predictive models Augmented with Isolated modality Ratings (PAIR) and demonstrate the potential of isolated modality ratings for enhancing multimodal emotion recognition. Our results suggest that perceptions of arousal are influenced primarily by auditory information, while perceptions of valence are more subjective and can be influenced by both visual and auditory information. The dataset is made publicly available.},
	url = {https://arxiv.org/abs/2202.10453},
	author = {P. Chua and D. Makris and K. Agres and G. Roig and D. Herremans}
}
@conference {2021,
	title = {Generating Lead Sheets with Affect: A Novel Conditional seq2seq Framework},
	booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)},
	year = {2021},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Virtual},
	url = {https://arxiv.org/abs/2104.13056},
	author = {D. Makris and K. Agres and D. Herremans}
}
@article {2021,
	title = {Music, Computing, and Health: A roadmap for the current and future roles of music technology for healthcare and well-being},
	journal = {Music \& Science},
	year = {2021},
	abstract = {The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop {\textquotedblleft}Music, Computing, and Health{\textquotedblright} was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech), and robotics. Following the discussions at the workshop, this article provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the article summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that (1) supports person-centered care and evidence-based treatments, and (2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The article provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field.},
	doi = {https://doi.org/10.1177/205920432199770},
	url = {https://journals.sagepub.com/doi/full/10.1177/2059204321997709},
	author = {K. Agres and Schaefer, Rebecca and Volk, Anja and Van Hooren, Susan and Holzapfel, Andr{\'e} and Dalla Bella, Simone and M{\"u}ller, Meinard and de Witte, Martina and D. Herremans and Ramirez Melendez, Rafael and Neerincx, Mark and Ruiz, Sebastian and Meredith, David and Dimitriadis, Theo and Magee, Wendy}
}
@conference {2020,
	title = {Acoustic prediction of flowrate: varying liquid jet stream onto a free surface},
	booktitle = {IEEE International Conference on Signal Processing and Communications (SPCOM)},
	year = {2020},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Blangladesh, India},
	abstract = {Information on liquid jet stream flow is crucial in many real world applications. In a large number of cases, these flows fall directly onto free surfaces (e.g. pools), creating a splash with accompanying splashing sounds. The sound produced is supplied by energy interactions between the liquid jet stream and the passive free surface. In this investigation, we collect the sound of a water jet of varying flowrate falling into a pool of water, and use this sound to predict the flowrate and flowrate trajectory involved. Two approaches are employed: one uses machine-learning models trained using audio features extracted from the collected sound to predict the flowrate (and subsequently the flowrate trajectory). In contrast, the second method directly uses acoustic parameters related to the spectral energy of the liquid-liquid interaction to estimate the flowrate trajectory. The actual flowrate, however, is determined directly using a gravimetric method: tracking the change in mass of the pooling liquid over time. We show here that the two methods agree well with the actual flowrate and offer comparable performance in accurately predicting the flowrate trajectory, and accordingly offer insights for potential real-life applications using sound.},
	author = {Balamurali BT and E. J. Aslim and Yun Shu Lynn Ng and Tricia Li Chuen Kuo and Jacob Shihang Chen and D. Herremans and Lay Guat Ng and J.M. Chen}
}
@conference {2020,
	title = {A dataset and classification model for Malay, Hindi, Tamil and Chinese music},
	booktitle = {13th Workshop on music and machine learning (MML) as part of ECML/PKDD},
	year = {2020},
	month = {08/2020},
	abstract = {In this paper we present a new dataset, with musical excepts from the three main ethnic groups in Singapore: Chinese, Malay and Indian (both Hindi and Tamil). We use this new dataset to train different classification models to distinguish the origin of the music in terms of these ethnic groups. The classification models were optimized by exploring the use of different musical features as the input. Both high level features, i.e., musically meaningful features, as well as low level features, i.e., spectrogram based features, were extracted from the audio files so as to optimize the performance of the different classification models.},
	author = {F. Nahar and K. Agres and Balamurali BT and D. Herremans}
}
@conference {2020,
	title = {The impact of Audio input representations on neural network based music transcription},
	booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)},
	year = {2020},
	month = {07/2020},
	address = {Glasgow},
	abstract = {This paper thoroughly analyses the effect of different input representations on polyphonic multi-instrument music transcription. We use our own GPU based spectrogram extraction tool, nnAudio, to investigate the influence of using a linear-frequency spectrogram, log-frequency spectrogram, Mel spectrogram, and constant-Q transform (CQT). Our results show that a 8.33\% increase in transcription accuracy and a 9.39\% reduction in error can be obtained by choosing the appropriate input representation (log-frequency spectrogram with STFT window length 4,096 and 2,048 frequency bins in the spectrogram) without changing the neural network design (single layer fully connected). Our experiments also show that Mel spectrogram is a compact representation for which we can reduce the number of frequency bins to only 512 while still keeping a relatively high music transcription accuracy.},
	url = {https://arxiv.org/abs/2001.09989},
	author = {K.W. Cheuk and K. Agres and D. Herremans}
}
@article {2019,
	title = {nnAudio: An on-the-fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolution Neural Networks},
	journal = {IEEE Access},
	year = {2020},
	abstract = {Converting time domain waveforms to frequency domain spectrograms is typically considered to be a prepossessing step done before model training. This approach, however, has several drawbacks. First, it takes a lot of hard disk space to store different frequency domain representations. This is especially true during the model development and tuning process, when exploring various types of spectrograms for optimal performance. Second, if another dataset is used, one must process all the audio clips again before the network can be retrained. In this paper, we integrate the time domain to frequency domain conversion as part of the model structure, and propose a neural network based toolbox, nnAudio, which leverages 1D convolutional neural networks to perform time domain to frequency domain conversion during feed-forward. It allows on-the-fly spectrogram generation without the need to store any spectrograms on the disk. This approach also allows back-propagation on the waveforms-to-spectrograms transformation layer, which implies that this transformation process can be made trainable, and hence further optimized by gradient descent. nnAudio reduces the waveforms-to-spectrograms conversion time for 1,770 waveforms (from the MAPS dataset) from 10.64 seconds with librosa to only 0.001 seconds for Short-Time Fourier Transform (STFT), 18.3 seconds to 0.015 seconds for Mel spectrogram, 103.4 seconds to 0.258 for constant-Q transform (CQT), when using GPU on our DGX work station with CPU: Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz Tesla v100 32Gb GPUs. (Only 1 GPU is being used for all the experiments.) We also further optimize the existing CQT algorithm, so that the CQT spectrogram can be obtained without aliasing in a much faster computation time (from 0.258 seconds to only 0.001 seconds).},
	doi = {10.1109/ACCESS.2020.3019084},
	url = {https://ieeexplore.ieee.org/document/9174990},
	author = {K.W. Cheuk and H. Anderson and K. Agres and D. Herremans}
}
@conference {2019,
	title = {Singing voice conversion with disentangled representations of singer and vocal technique using variational autoencoders},
	booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
	year = {2020},
	month = {05/2020},
	address = {Barcelona, Spain},
	url = {https://arxiv.org/abs/1912.02613},
	author = {Y.J. Luo and C.-C. Hsu and K. Agres and D. Herremans}
}
@inbook {2019,
	title = {The impact of musical structure on enjoyment and absorptive listening states in trance music},
	booktitle = {Music and Consciousness 2 - Worlds, Practices, Modalities},
	year = {2019},
	publisher = {Oxford University Press},
	organization = {Oxford University Press},
	edition = {Editors: Ruth Herbert, David Clarke, and Eric Clarke},
	address = {Oxfort, UK},
	abstract = {https://books.google.com.sg/books?id=DCOQDwAAQBAJ\&pg=PA254\&lpg=PA254\&dq=The+impact+of+musical+structure+on+enjoyment+and+absorptive+listening+states+in+trance+music\&source=bl\&ots=FsWlNXsys-\&sig=ACfU3U0K6dFJD7zbC917zG6HRFXLxVSdvQ\&hl=en\&sa=X\&ved=2ahUKEwjd05e7jdXkAhU_4nMBHe7bBQgQ6AEwBnoECAkQAQ$\#$v=onepage\&q=The\%20impact\%20of\%20musical\%20structure\%20on\%20enjoyment\%20and\%20absorptive\%20listening\%20states\%20in\%20trance\%20music\&f=false},
	issn = {9780198804352},
	url = {https://global.oup.com/academic/product/music-and-consciousness-2-9780198804352?cc=us\&lang=en\&$\#$},
	author = {K. Agres and L. Bigo and D. Herremans}
}
@conference {2019,
	title = {Learning Disentangled Representations of Timbre and Pitch for Musical Instrument Sounds Using Gaussian Mixture Variational Autoencoders},
	booktitle = {ISMIR},
	year = {2019},
	publisher = {ISMIR},
	organization = {ISMIR},
	address = {Delft, The Netherlands},
	url = {https://arxiv.org/abs/1906.08152},
	author = {Y.J. Luo and K. Agres and D. Herremans}
}
@conference {2019,
	title = {nnAudio: A PyTorch Audio Processing Tool Using 1D Convolution neural networks},
	booktitle = {ISMIR - Late Breaking Demo},
	year = {2019},
	month = {10/2019},
	address = {Delft, The Netherlands},
	author = {K.W. Cheuk and K. Agres and D. Herremans}
}
@conference {2019,
	title = {A novel music-based game with motion capture to support cognitive and motor function in the elderly},
	booktitle = {IEEE Conference on Games},
	year = {2019},
	publisher = {IEEE },
	organization = {IEEE },
	address = {London, UK},
	abstract = {This paper presents a novel game prototype that uses music and motion detection as preventive medicine for the elderly.
Given the aging populations around the globe, and the limited resources and staff able to care for these populations, eHealth solutions are becoming increasingly important, if not crucial, additions to modern healthcare and preventive medicine. Furthermore, because compliance rates for performing physical exercises are often quite low in the elderly, systems able to motivate and engage this population are a necessity. Our prototype uses music not only to engage listeners, but also to leverage the efficacy of music to improve mental and physical wellness. The game is based on a memory task to stimulate cognitive function, and requires users to perform physical gestures to mimic the playing of different musical instruments. To this end, the Microsoft Kinect sensor is used together with a newly developed gesture detection module in order to process users{\textquoteright} gestures. The resulting prototype system supports both cognitive functioning and physical strengthening in the elderly. },
	author = {K. Agres and S. Lui and D. Herremans}
}
@article {132,
	title = {From Context to Concept: Exploring Semantic Relationships in Music with Word2Vec},
	journal = {Neural Computing and Applications},
	year = {2018},
	keywords = {music, word2vec},
	doi = {10.1007/s00521-018-3923-1},
	url = {http://link.springer.com/article/10.1007/s00521-018-3923-1},
	author = {C.-H. Chuan and K. Agres and D. Herremans}
}
@article {119,
	title = {Minimally Simple Binaural Room Modelling Using a Single Feedback Delay Network},
	journal = {Journal of the Audio Engineering Society},
	volume = {66},
	year = {2018},
	month = {10/2018},
	pages = {791-807},
	abstract = {The most efficient binaural acoustic modeling systems use a multi-tap delay to generate accurately modeled early reflections, combined with a feedback delay network that produces generic late reverberation. We present a method of binaural acoustic simulation that uses one feedback delay network to simultaneously model both first-order reflections and late reverberation. The advantages are simplicity and efficiency. We compare the proposed method against the existing method of modeling binaural early reflections using a multi-tap delay line. Measurements of ISO standard evaluators including interaural correlation coefficient, decay time, clarity, definition, and center time, indicate that the proposed method achieves comparable level of accuracy as less-efficient existing methods. This method is implemented as an iOS application, and is able to auralize input signal directly without convolution and update in real time.},
	doi = {https://doi.org/10.17743/jaes.2018.0045},
	author = {N. Agus and H. Anderson and J.M. Chen and S. Lui and D. Herremans}
}
@article {117,
	title = {Perceptual evaluation of measures of spectral variance},
	journal = {Journal of the Acoustical Society of America},
	volume = {143},
	year = {2018},
	pages = {3300{\textendash}3311},
	abstract = {In many applications, it is desirable to achieve a signal that is as close as possible to ideal white noise. One example is in the design of an artificial reverberator, whereby there is a need for its lossless prototype output from an impulse input to be perceptually white as much as possible. The Ljung-Box test, the Drouiche test, and the Wiener Entropy{\textemdash}also called the Spectral Flatness Measure{\textemdash}are three well-known methods for quantifying the similarity of a given signal to ideal
white noise. In this paper, listening tests are conducted to measure the Just Noticeable Difference (JND) on the perception of white noise, which is the JND between ideal Gaussian white noise and noise with a specified deviation from the flat spectrum. This paper reports the JND values using one of these measures of whiteness, which is the Ljung-Box test. This paper finds considerable disagreement between the Ljung-Box test and the other two methods and shows that none of the methods is a significantly better predictor of listeners{\textquoteright} perception of whiteness. This suggests a need for a whiteness test that is more closely correlated to human perception.},
	doi = {10.1121/1.5040484},
	url = {https://asa.scitation.org/doi/10.1121/1.5040484},
	author = {N. Agus and H. Anderson and J.M. Chen and S. Lui and D. Herremans}
}
@mastersthesis {2018,
	title = {Real-Time Binaural Auralization},
	volume = {PhD},
	year = {2018},
	school = {Singapore University of Technology and Design},
	type = {PhD},
	address = {Singapore},
	author = {N. Agus}
}
@conference {120,
	title = {The Structure of Chord Progressions Influences Listeners{\textquoteright} Enjoyment and Absorptive States in EDM},
	booktitle = {15th International Conference on Music Perception and Cognition},
	year = {2018},
	month = {07/2018},
	address = {Sydney, Australia},
	author = {K. Agres and D. Herremans}
}
@article {97,
	title = {Generating guitar solos by integer programming},
	journal = {Journal of the Operational Research Society},
	year = {2017},
	pages = {971-985},
	doi = {https://doi.org/10.1080/01605682.2017.1390528},
	url = {http://www.tandfonline.com/eprint/Prks6BcYNEivw87vSu86/full},
	author = {Cunha, N. and Subramanian A. and Herremans, D}
}
@article {77,
	title = {Harmonic Structure Predicts the Enjoyment of  Uplifting Trance Music},
	journal = {Frontiers in Psychology, Cognitive Science},
	volume = {7},
	year = {2017},
	url = {http://journal.frontiersin.org/article/10.3389/fpsyg.2016.01999/full?\&utm_source=Email_to_authors_\&utm_medium=Email\&utm_content=T1_11.5e1_author\&utm_campaign=Email_publication\&field=\&journalName=Frontiers_in_Psychology\&id=230461},
	author = {K. Agres and D. Herremans and L. Bigo and D. Conklin}
}
@proceedings {99,
	title = {Music and Motion-Detection: A Game Prototype for Rehabilitation and Strengthening in the Elderly},
	year = {2017},
	month = {11/2017},
	address = {Singapore},
	author = {K. Agres and D. Herremans}
}
@proceedings {67,
	title = {The Effect of Repetitive Structure on Enjoyment in Uplifting Trance Music},
	year = {2016},
	month = {07/2016},
	pages = {280-282},
	address = {San Francisco},
	isbn = {1-876346-65-5 },
	author = {K. Agres and L. Bigo and D. Herremans and D. Conklin}
}
@proceedings {66,
	title = {Uma abordagem baseada em programa{\c c}{\~a}o linear inteira para a gera{\c c}{\~a}o de solos de guitarra},
	year = {2016},
	month = {09/2016},
	address = {Vit{\'o}ria, Brasil},
	author = {Cunha, N. and Subramanian A. and D. Herremans}
}
@conference {82,
	title = {The effect of repetitive structure on enjoyment and altered states in uplifting trance music},
	booktitle = {2nd International Conference on Music and Consciousness (MUSCON 2), Brighton},
	year = {2015},
	author = {Agres, K and Bigo, L and Herremans, D and Conklin, D}
}
